18 citations · 267 across the 76 of their papers we have counts for
10 papers · 2 filters
Complexity of Multiblock Convex-Concave Saddle-Point Problems with General Smooth Coupling
Nhat Trung Nguyen, Ivan Dorofeev, Irina Podlipnova +2
We study multiblock convex--concave saddle-point problems of the form , with separately charged partial-gradient oracles for all three objective c…
Accelerated Consensus and Decentralized Optimization over Slowly Varying Networks
Darina Dvinskikh, Anton Novitskii, Demyan Yarmoshik +3
We study average consensus over slowly time-varying networks and its application to decentralized optimization. We introduce WAVE, a windowed Chebyshev method that limits the accum…
Decentralized Optimization with Cross-Coupled Mixed Affine Constraints
Ewsey Obzherin, Ilya Khomchenko, Natalia Shelegeda +4
We study decentralized optimization with cross-coupled mixed affine constraints, where local and shared variables interact through two affine channels. We show that the intrinsic d…
Saddle-Point Problems with a Low-Dimensional Block Do Not Need Accurate Inner Solves
Ivan Fomin, Alexander V. Gasnikov
Many learning problems couple a high-dimensional block of parameters with a handful of adversarial or dual variables: worst-group risk over a few groups, or learning under a few co…
Convex-Concave Interpolation and Application of PEP to Bilinear-Coupled Saddle-Point Problem
Valery Krivchenko, Alexander Gasnikov, Dmitry Kovalev
The Performance estimation problem (PEP) approach reformulates finding the exact worst-case performance of an algorithm as the solution to an optimization problem. Tractable formul…
Sliding Methods for Hölder-Smooth Convex--Concave Minimax Optimization with Bilinear Coupling
Nhat Trung Nguyen, Alexander Gasnikov
We study convex-concave minimax optimization problems with bilinear coupling of the form w…
Decentralized Inexact Cubic Newton Method with Consensus Procedure
Artem Agafonov, Anton Novitskii, Alexander Rogozin +5
Distributed optimization is widely used in large-scale and privacy-preserving machine learning, where each agent stores a local objective and communicates only with its neighbors i…
Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation
Aleksandr Beznosikov, Georgiy Kormakov, Alexander Grigorievskiy +7
The objective of Vertical Federated Learning (VFL) is to collectively train a model using features available on different devices while sharing the same users. This paper focuses o…
Decentralized Optimization with Mixed Affine Constraints
Demyan Yarmoshik, Nhat Trung Nguyen, Alexander Rogozin +1
This paper considers decentralized optimization of convex functions with mixed affine equality constraints involving both local and global variables. Constraints on global variable…
Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity
Boris Prokhorov, Semyon Chebykin, Alexander Gasnikov +1
This paper deals with stochastic optimization problems involving Markovian noise with a zero-order oracle. We present and analyze a novel derivative-free method for solving such pr…